{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Scatter Plots"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another commonly used plot type is the simple scatter plot, a close cousin of the line plot.\n",
    "Instead of points being joined by line segments, here the points are represented individually with a dot, circle, or other shape.\n",
    "We’ll start by setting up the notebook for plotting and importing the packages we will use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter Plots with plt.plot\n",
    "\n",
    "In the previous chapter we looked at using `plt.plot`/`ax.plot` to produce line plots.\n",
    "It turns out that this same function can produce scatter plots as well (see the following figure):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 10, 30)\n",
    "y = np.sin(x)\n",
    "\n",
    "plt.plot(x, y, 'o', color='black');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The third argument in the function call is a character that represents the type of symbol used for the plotting. Just as you can specify options such as `'-'` or `'--'` to control the line style, the marker style has its own set of short string codes. The full list of available symbols can be seen in the documentation of `plt.plot`, or in Matplotlib's [online documentation](https://matplotlib.org/stable/api/_as_gen/matplotlib.markers.MarkerStyle.html). Most of the possibilities are fairly intuitive, and a number of the more common ones are demonstrated here (see the following figure):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rng = np.random.default_rng(0)\n",
    "for marker in ['o', '.', ',', 'x', '+', 'v', '^', '<', '>', 's', 'd']:\n",
    "    plt.plot(rng.random(2), rng.random(2), marker, color='black',\n",
    "             label=\"marker='{0}'\".format(marker))\n",
    "plt.legend(numpoints=1, fontsize=13)\n",
    "plt.xlim(0, 1.8);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For even more possibilities, these character codes can be used together with line and color codes to plot points along with a line connecting them (see the following figure):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y, '-ok');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additional keyword arguments to `plt.plot` specify a wide range of properties of the lines and markers, as you can see in the following figure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y, '-p', color='gray',\n",
    "         markersize=15, linewidth=4,\n",
    "         markerfacecolor='white',\n",
    "         markeredgecolor='gray',\n",
    "         markeredgewidth=2)\n",
    "plt.ylim(-1.2, 1.2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These kinds of options make `plt.plot` the primary workhorse for two-dimensional plots in Matplotlib.\n",
    "For a full description of the options available, refer to the [`plt.plot` documentation](https://matplotlib.org/3.5.0/api/_as_gen/matplotlib.pyplot.plot.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter Plots with plt.scatter\n",
    "\n",
    "A second, more powerful method of creating scatter plots is the `plt.scatter` function, which can be used very similarly to the `plt.plot` function (see the following figure):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y, marker='o');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The primary difference of `plt.scatter` from `plt.plot` is that it can be used to create scatter plots where the properties of each individual point (size, face color, edge color, etc.) can be individually controlled or mapped to data.\n",
    "\n",
    "Let's show this by creating a random scatter plot with points of many colors and sizes.\n",
    "In order to better see the overlapping results, we'll also use the `alpha` keyword to adjust the transparency level (see the following figure):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rng = np.random.default_rng(0)\n",
    "x = rng.normal(size=100)\n",
    "y = rng.normal(size=100)\n",
    "colors = rng.random(100)\n",
    "sizes = 1000 * rng.random(100)\n",
    "\n",
    "plt.scatter(x, y, c=colors, s=sizes, alpha=0.3)\n",
    "plt.colorbar();  # show color scale"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the color argument is automatically mapped to a color scale (shown here by the `colorbar` command), and that the size argument is given in pixels.\n",
    "In this way, the color and size of points can be used to convey information in the visualization, in order to visualize multidimensional data.\n",
    "\n",
    "For example, we might use the Iris dataset from Scikit-Learn, where each sample is one of three types of flowers that has had the size of its petals and sepals carefully measured (see the following figure):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()\n",
    "features = iris.data.T\n",
    "\n",
    "plt.scatter(features[0], features[1], alpha=0.4,\n",
    "            s=100*features[3], c=iris.target, cmap='viridis')\n",
    "plt.xlabel(iris.feature_names[0])\n",
    "plt.ylabel(iris.feature_names[1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A full-color version of this plot is available in the [online version](http://github.com/jakevdp/PythonDataScienceHandbook) of the book.\n",
    "\n",
    "We can see that this scatter plot has given us the ability to simultaneously explore four different dimensions of the data:\n",
    "the (*x*, *y*) location of each point corresponds to the sepal length and width, the size of the point is related to the petal width, and the color is related to the particular species of flower.\n",
    "Multicolor and multifeature scatter plots like this can be useful for both exploration and presentation of data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## plot Versus scatter: A Note on Efficiency\n",
    "\n",
    "Aside from the different features available in `plt.plot` and `plt.scatter`, why might you choose to use one over the other? While it doesn't matter as much for small amounts of data, as datasets get larger than a few thousand points, `plt.plot` can be noticeably more efficient than `plt.scatter`.\n",
    "The reason is that `plt.scatter` has the capability to render a different size and/or color for each point, so the renderer must do the extra work of constructing each point individually.\n",
    "With `plt.plot`, on the other hand, the markers for each point are guaranteed to be identical, so the work of determining the appearance of the points is done only once for the entire set of data.\n",
    "For large datasets, this difference can lead to vastly different performance, and for this reason, `plt.plot` should be preferred over `plt.scatter` for large datasets."
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "encoding": "# -*- coding: utf-8 -*-",
   "formats": "ipynb,md"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
